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The Data Warehouse Toolkit

The Data Warehouse Toolkit - Third Edition

By : Ralph Kimball, Margy Ross
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The Data Warehouse Toolkit

The Data Warehouse Toolkit

By: Ralph Kimball, Margy Ross

Overview of this book

The volume of data continues to grow as warehouses are populated with increasingly atomic data and updated with greater frequency. Dimensional modeling has become the most widely accepted approach for presenting information in data warehouse and business intelligence (DW/BI) systems. The goal of this book is to provide a one-stop shop for dimensional modeling techniques. The book is authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence. The book begins with a primer on data warehousing, business intelligence, and dimensional modeling, and you’ll explore more than 75-dimensional modeling techniques and patterns. Then you’ll understand dimension tables in-depth to get a good grip on retailing and moved towards the topics of inventory. Moving ahead, you’ll learn how to use this book for procurement, order management, accounting, customer relationship management, and many more business sectors. By the end of this book, you’ll be able to gather all the essential knowledge, practices, and patterns for designing dimensional models.
Table of Contents (31 chapters)
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1
Cover
2
Title Page
3
Copyright
4
About the Authors
5
Credits
6
Acknowledgements
29
Index
30
Advertisement
31
End User License Agreement

Factless Fact Tables

There is one important question that cannot be answered by the previous retail sales schema: What products were on promotion but did not sell? The sales fact table records only the SKUs actually sold. There are no fact table rows with zero facts for SKUs that didn’t sell because doing so would enlarge the fact table enormously.

In the relational world, a promotion coverage or event fact table is needed to answer the question concerning what didn’t happen. The promotion coverage fact table keys would be date, product, store, and promotion in this case study. This obviously looks similar to the sales fact table you just designed; however, the grain would be significantly different. In the case of the promotion coverage fact table, you’d load one row for each product on promotion in a store each day (or week, if retail promotions are a week in duration) regardless of whether the product sold. This fact table enables you to see the relationship between...

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The Data Warehouse Toolkit
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